Microscopic analysis of histologic brain sections yields important information about the organization and composition of the nervous system, particularly in modern studies focusing on (neuro-) genomics, transcriptomics, proteomics, and connectomics. However, identification of distinct brain regions in a section under study with the currently available tools (printed or digital brain atlases) is tedious and cumbersome and can even be misleading. This Direct-to-Phase-II project aims to develop a novel system for automatically identifying distinct brain regions in a section of a mouse brain being studied under a microscope by augmenting the section with graphical delineations and annotations of neuroanatomic regions in real-time (henceforth referred to as brain navigation information) - the proposed Brain Navigation System(tm) (BNS). BNS will work as follows: after placing a section of a mouse brain under a microscope the BNS software will (i) automatically generate a virtual slide of the section, (ii) match the virtual slide image with the best fitting oblique angl slice through a digital 3D volume reconstruction of reference histology images from a reference mouse brain (performed with the BNS Registration Match Finding and Identification Tool), (iii) retrieve corresponding brain region delineations and annotations from a related digital 3D volume reconstruction of reference brain region delineations (also performed with the BNS Registration Match Finding and Identification Tool), and (iv) augment the section under study with brain navigation information either on the computer monitor or within the eyepieces of the microscope using our existing Lucivid(r) device (performed with the BNS Visualization and Navigation Tool). Brain navigation information will be retrieved from a special, digital BNS Mouse Brain Atlas consisting of reference histology images and related reference brain region delineations that will be created with a special BNS Atlas Generation Tool. A BNS Waxholm Bridge Tool will allow researchers to directly correlate their experimental specimens with a number of important reference tools on the internet such as the Allen Brain Connectivity Map, etc. The benefits of BNS for neuroscience research -and society in general- will be (i) objectivity, validity, reliability, and substantial time savings for identifying brain regions inspcted under the microscope, (ii) the potential for analyzing many more brain regions than is currently done, thereby (iii) obtaining valuable additional information from brain sections that is currently not explored, finally resulting in (iv) an improved basis for understanding how changes in neural activity contributes to mental disorders. During pilot work performed in preparation for this project we proved feasibility of this novel Registration Match Finding technology by developing prototype software whose performance clearly exceeds the current state-of- the-art in the field. Work in Phase II will focus on (i) developing the production version of the different tools of BNS outlined above, (ii) combining this technology with motorized microscope and XY specimen stage control, and (iii) validating the full functionality of BNS.